Data visualization is an essential part of the statistical analysis process, both for exploratory analyses and summarizing findings.

Exploratory Data Analysis

• Used for data quality checks

• Help explore and understand the data

• Typically, not seen by anyone else

Polished Data Visualization

• Used to summarize data in presentations or papers

• Should stand alone with appropriate titles, axes, labels, and captions

There are many tools for creating spatial figures (GIS software, Tableau, etc…), but we will exclusively use R and the wide range of packages within it.

In particular, we will use:

• ggplot2

• ggmap

• leaflet

• RgoogleMaps

• and many others…

 mykey <- read_file('./google_api.txt')
  register_google(key = mykey)
  myMap <- get_map(location = c(lon = - 74,lat = 40.75),
                 source = "google",
                 maptype = "roadmap", crop = FALSE,
                 zoom = 11, api_key = mykey)

  ggmap(myMap) + 
    geom_point(aes(x=Lon, y=Lat), alpha=.03, size=.5, data=uber) + 
    labs(title = 'Location of Uber pickups on May 1, 2014 for NYC Destinations', 
    caption = 'source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city') + 
    xlab('') + ylab('') +
    theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

1. Include useful background for appropriate context: there are several approaches for acquiring maps in R. Sometimes streets may be more useful, but in other situation a terrain image might be more relevant.
2. With a point patterns, use transparency or heat map summaries to distinguish between areas of higher and lower intensity.
3. Include useful titles, labels, and where appropriate, captions (all figures). These figures should stand alone.
4. Sources should be cited in figures.

Luckily, for all of us, this course will not consist of me talking for 75 minutes. Rather, active learning components will be interspersed within the day. In general, we will spend some time talking about a topic and then I will give you time to work through data visualization or analysis.

To facilitate the active learning sessions, I'd recommend cloning the repo at the start of class. For instance, here is the repo for today https://github.com/Stat534/Lecture3. Using R Studio to create a local project is one way to do this, but you won't have push access to my repo.

seattle <- read_csv('./SeattlePolice.csv')

## Parsed with column specification:
## cols(
##   CAD.Event.Number = col_double(),
##   Event.Clearance.Description = col_character(),
##   Event.Clearance.SubGroup = col_character(),
##   Event.Clearance.Group = col_character(),
##   Census.Tract = col_double(),
##   Longitude = col_double(),
##   Latitude = col_double(),
##   Year = col_integer(),
##   Month = col_integer(),
##   Day = col_integer()
## )

A collaborator suggests that there may a spatial relationship between the police calls in the Seattle Data Set. How would you calculate the distance between those points?

A collaborator suggests that there may a spatial relationship between the police calls in the Seattle Data Set. How would you calculate the distance between those points?

As a follow up:

• what are the units you have calculated?
• are they consistent across latitude?

Map projections are a representation of a surface on a plane. Specifically, functions are designed to map the geographical coordinate system \((\lambda, \phi)\) to an approach such that:

\[x = f(\lambda, \phi), \; \; y = g(\lambda, \phi),\]

where

• Euclidean
• geodesic

• Google API for ggmap

• leaflet demo

• Geocomputation with R: mapping overview

• Creating-maps-in-R

• Making Maps with R